






'''simple docstring'''





import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Tuple			,       A					:			List[str]			,       A					:			Any=5		)    ->   Optional[Any]:
			# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
			assert masked_input.count('''<mask>'''		) == 1
			UpperCAmelCase_      :       str          =       torch.tensor(tokenizer.encode(A			,       add_special_tokens=A		)		).unsqueeze(0		)  # Batch size 1
			UpperCAmelCase_      :       Any          =       model(A		)[0]  # The last hidden-state is the first element of the output tuple
			UpperCAmelCase_      :       Union[str, Any]          =       (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
			UpperCAmelCase_      :       Optional[int]          =       logits[0, masked_index, :]
			UpperCAmelCase_      :       List[Any]          =       logits.softmax(dim=0		)
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       prob.topk(k=A			,       dim=0		)
			UpperCAmelCase_      :       Any          =       ''' '''.join(
			    [tokenizer.convert_ids_to_tokens(indices[i].item()		) for i in range(len(A		)		)]		)
			UpperCAmelCase_      :       str          =       tokenizer.mask_token
			UpperCAmelCase_      :       Tuple          =       []
			for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''		)		):
						UpperCAmelCase_      :       int          =       predicted_token_bpe.replace('''\u2581'''			,       ''' '''		)
						if " {0}".format(A		) in masked_input:
									topk_filled_outputs.append(
									    (
									        masked_input.replace(''' {0}'''.format(A		)			,       A		),
									        values[index].item(),
									        predicted_token,
									    )		)
						else:
									topk_filled_outputs.append(
									    (
									        masked_input.replace(A			,       A		),
									        values[index].item(),
									        predicted_token,
									    )		)
			return topk_filled_outputs


_UpperCamelCase						:						str  =					CamembertTokenizer.from_pretrained('camembert-base')
_UpperCamelCase						:						int  =					CamembertForMaskedLM.from_pretrained('camembert-base')
model.eval()

_UpperCamelCase						:						Tuple  =					'Le camembert est <mask> :)'







print(fill_mask(masked_input, model, tokenizer, topk=3))










'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


_UpperCamelCase						:						Tuple  =					{
    'configuration_audio_spectrogram_transformer': [
        'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ASTConfig',
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase						:						Optional[int]  =					[
						    'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'ASTForAudioClassification',
						    'ASTModel',
						    'ASTPreTrainedModel',
						]

try:
						if not is_speech_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase						:						int  =					['ASTFeatureExtractor']

if TYPE_CHECKING:
						from .configuration_audio_spectrogram_transformer import (
						    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    ASTConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_audio_spectrogram_transformer import (
												    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ASTForAudioClassification,
												    ASTModel,
												    ASTPreTrainedModel,
												)

						try:
												if not is_speech_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
						import sys

						_UpperCamelCase						:						List[str]  =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


